Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 2231 2023-07-10 12:08:22 |
2 format Meta information modification 2231 2023-07-13 04:18:28 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Anand K, K.; Mishra, A.K. Market Connectedness and Volatility Spillovers. Encyclopedia. Available online: (accessed on 06 December 2023).
Anand K K, Mishra AK. Market Connectedness and Volatility Spillovers. Encyclopedia. Available at: Accessed December 06, 2023.
Anand K, Kamesh, Aswini Kumar Mishra. "Market Connectedness and Volatility Spillovers" Encyclopedia, (accessed December 06, 2023).
Anand K, K., & Mishra, A.K.(2023, July 10). Market Connectedness and Volatility Spillovers. In Encyclopedia.
Anand K, Kamesh and Aswini Kumar Mishra. "Market Connectedness and Volatility Spillovers." Encyclopedia. Web. 10 July, 2023.
Market Connectedness and Volatility Spillovers
The growing globalization and interactions between markets and countries have made the market co-movements more complex in analytics; with the ongoing innovations and contagions or crises, the literature on connectedness and spillover would be of outstanding contribution and interest to the policymakers, investors, and hedgers for portfolio diversification and risk management.
integration bibliometric analysis meta-analysis

1. Introduction

The past decade has exhibited a significant perturbation of the markets, with consequent international crises. This has led to research, as reported in the literature, on financial market integration, connectedness, and co-movements, and there is a spillover, with implications for policy and portfolio diversification. While financial turmoil threatens the market’s stability, it also threatens the stability of the country’s economic activities. Two of the main non-trivial problems decision-makers and investors face are managing optimal portfolios and asset allocations and adopting policies that will stabilize the economy and markets [1].
Ref. [2] defined financial shocks and transmissions, or causality in the variance between markets and volatility spillovers. Ref. [3] called this variance contagion, usually detected during events or crises as they increase cross-market linkage or co-movements. Later, studies [1][4][5] explored the information dissemination and asymmetries surrounding market connectedness and spillovers, which pose another non-trivial problem, as there is a lack of knowledge on the market’s reaction towards these asymmetries.
Ref. [6] proposed that the increase in volatility converges from innovation and development, investor protection, financial openness, and the country’s level of risk. Studies have addressed determinants of why and how these volatility transmissions occur. Most triggering factors are macro- and economic-policy-based, and some are potentially due to the firm’s origin or country of origin [7][8][9][10][11]. Thus, market connectedness and volatility spillovers grabbed researchers’ interest in market information dissemination and how to help this behavior bear fruit, so to speak, from markets, through hedging and diversification.

2. Citation Analysis and Visualization

2.1. Co-Citation Mapping

Researchers conducted the co-citation analysis using Bibliometrix. The minimum citation threshold was set to 12 and the walk trap algorithm was implemented for citation networking. Researchers obtained three main clusters—red, blue and green—corresponding to asymmetries in market connectedness, major determinants and macroeconomic variables or factors that influence the market connectedness and spillovers, and dynamic market spillover among different markets and sectors, respectively (presented in Figure 1).
To add the element of thematic evolution and to identify the primary focus of research for each cluster, Researchers conducted a content analysis; Researchers extracted the core papers using the PageRank metric and evaluated them to corroborate their linkages and integrity [12]. The content analysis revealed that extensive studies have been conducted on oil, which should be given special consideration; thus, Researchers treated them as a distinct stream. The identified streams consist of (1) asymmetries in market connectedness, (2) macro factors that impact market connectedness and spillovers, (3) the role of oil in spillover and hedging portfolios and (4) dynamic cross-market connectedness and spillovers.

2.2. Co-Authorship Visualization

Researchers conducted the co-authorship analysis, with the minimum threshold of three co-authored papers. This provides a unique perspective on the evolution of these research streams over time with these authors [13]. The authors do not constitute a vast network; however, the network is pertinent, and the affiliations are strengthening over time. The analysis outcome is depicted in Figure 2.
Figure 2. Results of Co-authorship analysis. Source: same as Figure 1.

2.3. Co-Word and Thematic Analysis

Researchers performed co-word analysis followed by thematic analysis to illustrate the evolution of keywords and themes within each stream. Figure 3 depicts the outcome of the keyword analysis, which provides the evolution of keyword usage by the authors and the growth of the current literature over time.
Figure 3. Results of co-word analysis. Source: same as Figure 1.
Researchers employed concepts of centrality and density to identify core keywords. Researchers analyzed their evolution across time and the entire sample period, categorizing the themes into base, motor, isolated, and declining or emerging themes, as shown in Figure 4.
Figure 4. Results of thematic analysis using author’s keywords. Source: same as Figure 1.

3. Content Analysis of the Four Clusters

3.1. Asymmetries in Market Connectedness

Ref. [14] analyzed the asymmetries in the forex market of AUD, GBP, CAD, EUR, JPY, and CHF during the 2007–2015 period. The Global Financial Crisis (GFC, 2008) mainly induced good volatility, and Japan became the primary receiver. Ref. [15] conducted ARMAX-GARCH to investigate the volatility spillover of the commodity market (energy, industrial metals, precious metals, oil seeds) to the sovereign CDS of 23 emerging and frontier markets and noticed significant but inconsistent transmission over time. However, the results vary over time and commodities that are considered.
Ref. [16] verified the statistical properties of asymmetries in return connectedness between the Asian currencies from 1994 to 2019. They proposed that considering the size of the return shock is proportional to connectedness and crucial for the development of rigorous portfolio diversification and policies. The study argues that planning policies and schemes should take into account the prospects of the market’s potential positive and negative asymmetries. According to Ref. [17], policymakers should exercise caution when simultaneously investing in currency and energy markets. Assessment of structural breaks improves a deeper comprehension of the persistent behavior of the market [17][18].
Large asymmetric transmission exists between oil and equities, whereas bilateral transmission is negligible. Instead of using historical volatility, Ref. [19] argued that implied volatility provides additional information on the connectedness and risk transmission between different markets and countries. Ref. [20] demonstrated that China’s and global oil prices are asymmetries. According to Ref. [9], the Asian stock market co-moves without discernible asymmetry between them.
Ref. [21] defined asymmetries in OPEC’s announcement as the cut, maintain, and hike decisions and investigated their influence on cereal products. They confirm a significant asymmetric effect of oil and gasoline on cereals over a continuous period, disregarding economic fundamentals and the prices of the commodities; they referred to decisions to make cuts as bad news and decisions to maintain the status quo as good news.
Refs. [19][22][23] argued that market volatility spillover and asymmetries are crucial when constructing hedge ratios and optimal portfolios. However, in accordance with Ref. [24], market connectedness is not extremely susceptible to information spillover among the ASEAN markets.

3.2. Macro Factors Impact on Market Connectedness and Spillovers

Ref. [24] demonstrated that market size does not play a significant role in market integration. After removing the effects of the global market, they discovered that the level of connectedness drops drastically. Refs. [8][24] showed that capital account restrictions, exchange restrictions, and capital control play a unique role in the integration and information dissemination among the markets. Different regimes (tax, size, stability, technological advancements) have distinct effects on connectedness.
Ref. [18] supported the notion of flight-to-quality, i.e., shifting to safe haven assets during times of crisis. In his study, VIX is considered the ideal hedging instrument and unconditional against developed markets where currency treasuries (likely Yen and USD) are contingent on the market conditions. For developed markets, the safe haven assets exhibit relatively low-risk exposure, less than unity; however, this exposure increased during the GFC. As per Ref. [25], the macroeconomic developments in the European Economic and Monetary Union (EMU) initiates the bond–stock market integration.
Ref. [20] asserted that arbitrage plays a vital role when the oil market’s prices are below the threshold level by using the threshold VECM, and defined these threshold effects as originating from the transaction cost. Ref. [8] advocated four linkages, which are economic, financial, information capacity, and industrial similarity, and revealed that information capacity and industrial similarity are highly prone to developed markets whereas economic and financial integration are crucial in developing and developed markets.
Ref. [7] provided evidence that future trading impacts the spot stock prices but not the futures spreads, and the impact is not due to the exogenous factors but rather to broad market factors. Ref. [10] added that during extreme events, the currency carry trade and stock markets affect each other, i.e., a bilateral spillover exists in times of events.
EPU has an enormous impact when the stock market confronts downside risk. When stimulative schemes are enacted, the upside risk is sensitive to the EPU but shifts in the opposite direction regarding disputes or contagion [26]. However, Ref. [27] observed that EPU’s effect on dynamic connectedness is regime-dependent.

3.3. Role of Oil in Market Spillovers and Hedging

Ref. [28] stated that the influence of natural gas has not received less attention compared to crude oil, despite the former having environmental benefits. They found an optimal hedging ratio between stocks and oil and natural gas, i.e., a dollar invested in stock should be hedged with a cent in natural gas futures or short selling of the oil futures. When equities possess significant asymmetries, they found that Indian investors tend to favor natural gas over oil for hedging. Similarly, as per [29], a dollar in Brent crude oil should be hedged with 10 cents in DJASIA and 30 cents in Italy stocks. In this regard, the hedging is contingent on the time horizon, and the uncertainty depends on the contingents. They observed that during ESDC, hedging is more costly than during the GFC and oil price bursts. Higher oil prices can affect vulnerable economies, break the exchange rates, and deteriorate the performance of the stock returns. It is also emphasized that higher oil prices improve the oil-exporting countries’ fiscal stability.
Ref. [30] enriched the literature by filling the gap by examining the impact of the oil volatility on the stocks of heavy oil importing countries; he found that oil played a significant role post-crisis (GFC). The oil volatility had little effect on Lebanon, a country which is dominated by banking and services, whereas Jordan has shown a significant response to the oil shocks. The presence of foreign investors influenced small countries such as Morocco and Tunisia.
Ref. [31] analyzed the oil and exchange rate connectedness and spillover of the major oil exporting and importing countries. He segregated the oil shocks into demand, supply and risk following [28] segregation. The study found that the demand and risk shock significantly affect the exchange rates and increase after the crisis; the author asserts these phenomena possess potential forecasting advantages that can be used to construct an international hedging portfolio. He states that it is also important for policymakers to pursue oil shocks for trade balance as part of macro-level regulation and policing.
Ref. [32] tried to capture the role of exogenous shocks on the information transmission of the oil–equity uncertainty index. They figured that the exogenous shocks impacted the volatility spillovers between the oil and equity uncertainty index post-crisis but was insignificant pre-crisis. Ref. [33] found that the exchange rates and interest rates and the oil prices co-move over time, and the important oil importing and exporting countries’ exchange rates were affected by the crude oil, and importing countries were affected relatively higher than exporting countries in their study. They noted that the rise in oil prices results in exchange rate appreciation.

3.4. Dynamic Cross-Markets Connectedness and Spillover

Ref. [24] found that the level of cross-market integration within the ASEAN market is not as high as is commonly perceived when considering the global market influence; the interconnectedness is relatively low. They noticed that the cross-market connectedness decreases when the world market factors are filtered out. Ref. [28] demonstrated that the dynamic model (DCC) captures the asymmetries in the market better compared to the constant model (CCC). In their sample, oil serves a crucial role in cross-market spillovers. Studies have considered the dynamic models for cross-market analysis to incorporate the geopolitical risk that transits into the world market systems [34]. In addition, they analyzed the geopolitical risks of BRICS, oil, and gold and found that they varied over time and frequency frame and intensified in the short term. For short-term investors, the impact of the geopolitical risk has to be the main focal element, as the study stated that the GFC and the Ukrainian crisis caused numerous spikes in the spillover. They have stated that gold and oil have a hedging function during geopolitical events, and the oil–gold nexus has to be the main concern for the hedgers.
In the crypto market, investors are not only acclimated to positive returns. Ref. [35] noticed that the general notion of Bitcoin does not apply to crypto market connectedness, and Bitcoin has transitioned from a net contributor to a net receiver over time. At the same time, other significant currencies entered the market, and Litecoin became the epicenter of connectedness. Studies have observed that if return and trading volumes are connected, there is a significant linkage between the return connectedness and the trading volume; however, Ref. [36] asserted that the volatility of Bitcoin exchange markets depends on the asset’s withdrawal rather than the trading volume. Ref. [37] demonstrated that the LME nonferrous metal futures impact SFE nonferrous metal and added that the GFC intensified the impact pattern. Ref. [25] observed bond and stock integration and discussed the reason why it is happening. They pointed out that international bond and stock integration are typically characterized by dynamism. In the event that monetary policies play a role in these linkages, bonds usually have the upper hand.
Ref. [38] found shreds of evidence in the spillover from the developed stock market to the emerging market; however, the linkage is tenuous. Studies found that Asian countries’ stock markets exhibit co-movements and strengthened after the impact of the GFC. However, the interaction between Asia and the US was minimal, and hardly any co-movements ensued. Ref. [39] found that the asymmetry transmission can be discerned as stabilizing unit between the stock markets and should be regarded as a hedging tool. He found a degree of transmission between the developed markets and the emerging Asian markets.


  1. BenSaïda, A. Good and bad volatility spillovers: An asymmetric connectedness. J. Financ. Mark. 2019, 43, 78–95.
  2. Engle, R.F.; Ito, T.; Wenlong, L. Metor Shower? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market. Econometrica 1990, 58, 525–542.
  3. Forbes, K.J.; Rigobon, R. No contagion, only interdependence: Measuring stock market comovements. J. Financ. 2002, 57, 2223–2261.
  4. Baruník, J.; Křehlík, T. Measuring the frequency dynamics of financial connectedness and systemic risk. J. Financ. Econ. 2018, 16, 271–296.
  5. Baruník, J.; Kočenda, E.; Vácha, L. Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. J. Financ. Mark. 2016, 27, 55–78.
  6. Brown, G. Why Are U.S. Stocks More Volatile ? J. Financ. 2012, LXVII, 1329–1370.
  7. Chang, E.C.; Cheng, J.W.; Pinegar, J. Does futures trading increase stock market volatility? The case of the Nikkei stock index futures markets. J. Bank Financ. 1999, 23, 727–753.
  8. Liu, L. International stock market interdependence: Are developing markets the same as developed markets? J. Int. Financ. Mark. Inst. Money 2013, 26, 226–238.
  9. Narayan, S.; Sriananthakumar, S.; Islam, S. Stock market integration of emerging Asian economies: Patterns and causes. Econ. Model. 2014, 39, 19–31.
  10. Liu, C.-L.; Yang, H.-F. Systemic risk in carry-trade portfolios. Financ. Res. Lett. 2016, 20, 40–46.
  11. Hu, M.; Zhang, D.; Ji, Q.; Wei, L. Macro factors and the realized volatility of commodities: A dynamic network analysis. Resour. Policy 2020, 68, 101813.
  12. Xu, X.; Chen, X.; Jia, F.; Brown, S.; Gong, Y.; Xu, Y. Supply chain fi nance: A systematic literature review and bibliometric analysis. Int. J. Prod. Econ. 2018, 204, 160–173.
  13. Acedo, F.J.; Casillas, J.C. Current paradigms in the international management field: An author co-citation analysis. Int. Bus. Rev. 2005, 14, 619–639.
  14. Baruník, J.; Kočenda, E.; Vácha, L. Asymmetric volatility connectedness on the forex market. J. Int. Money Financ. 2017, 77, 39–56.
  15. Bouri, E.; de Boyrie, M.E.; Pavlova, I. Volatility transmission from commodity markets to sovereign CDS spreads in emerging and frontier countries. Int. Rev. Financ. Anal. 2017, 49, 155–165.
  16. Bouri, E.; Lucey, B.; Saeed, T.; Vo, X.V. Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis. Int. Rev. Financ. Anal. 2020, 72, 101605.
  17. Mensi, W.; Hammoudeh, S.; Yoon, S.-M. Structural breaks, dynamic correlations, asymmetric volatility transmission, and hedging strategies for petroleum prices and USD exchange rate. Energy Econ. 2014, 48, 46–60.
  18. Balcilar, M.; Demirer, R.; Gupta, R.; Wohar, M.E. The effect of global and regional stock market shocks on safe haven assets. Struct. Chang. Econ. Dyn. 2020, 54, 297–308.
  19. Maghyereh, A.I.; Awartani, B.; Bouri, E. The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes. Energy Econ. 2016, 57, 78–93.
  20. Liu, L.; Chen, C.-C.; Wan, J. Is world oil market ‘one great pool’ ?: An example from China’s and international oil markets. Econ. Model. 2013, 35, 364–373.
  21. Mensi, W.; Hammoudeh, S.; Nguyen, D.K.; Yoon, S.-M. Dynamic spillovers among major energy and cereal commodity prices. Energy Econ. 2014, 43, 225–243.
  22. Maghyereh, A.I.; Awartani, B.; Tziogkidis, P. Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries. Energy Econ. 2017, 68, 440–453.
  23. Chang, C.-L.; McAleer, M.; Tansuchat, R. Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ. 2010, 32, 1445–1455.
  24. Wu, F. Stock market integration in East and Southeast Asia: The role of global factors. Int. Rev. Financ. Anal. 2019, 67, 101416.
  25. Kim, S.-J.; Moshirian, F.; Wu, E. Evolution of international stock and bond market integration: Influence of the European Monetary Union. J. Bank. Financ. 2006, 30, 1507–1534.
  26. Wang, Z.; Li, Y.; He, F. Asymmetric volatility spillovers between economic policy uncertainty and stock markets: Evidence from China. Res. Int. Bus. Financ. 2020, 53, 101233.
  27. Mokni, K.; Hammoudeh, S.; Ajmi, A.N.; Youssef, M. Does economic policy uncertainty drive the dynamic connectedness between oil price shocks and gold price? Resour. Policy 2020, 69, 101819.
  28. Kumar, S.; Pradhan, A.K.; Tiwari, A.K.; Kang, S.H. Correlations and volatility spillovers between oil, natural gas, and stock prices in India. Resour. Policy 2019, 62, 282–291.
  29. Mensi, W.; Hammoudeh, S.; Vo, X.V.; Kang, S.H. Volatility spillovers between oil and equity markets and portfolio risk implications in the US and vulnerable EU countries. J. Int. Financ. Mark. Inst. Money 2021, 75, 101457.
  30. Bouri, E. Oil volatility shocks and the stock markets of oil-importing MENA economies: A tale from the financial crisis. Energy Econ. 2015, 51, 590–598.
  31. Malik, F.; Umar, Z. Dynamic connectedness of oil price shocks and exchange rates. Energy Econ. 2019, 84, 104501.
  32. Li, L.; Yin, L.; Zhou, Y. Exogenous shocks and the spillover effects between uncertainty and oil price. Energy Econ. 2016, 54, 224–234.
  33. Kim, J.-M.; Jung, H. Dependence Structure between Oil Prices, Exchange Rates, and Interest Rates. Energy J. 2018, 39, 233–258.
  34. Li, Y.; Huang, J.; Gao, W.; Zhang, H. Analyzing the time-frequency connectedness among oil, gold prices and BRICS geopolitical risks. Resour. Policy 2021, 73, 102134.
  35. Ji, Q.; Bouri, E.; Lau, C.K.M.; Roubaud, D. Dynamic connectedness and integration in cryptocurrency markets. Int. Rev. Financ. Anal. 2018, 63, 257–272.
  36. Ji, Q.; Bouri, E.; Kristoufek, L.; Lucey, B. Realised volatility connectedness among Bitcoin exchange markets. Financ. Res. Lett. 2019, 38, 101391.
  37. Kang, S.H.; McIver, R.; Yoon, S.-M. Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Econ. 2017, 62, 19–32.
  38. Li, H.; Majerowska, E. Testing stock market linkages for Poland and Hungary: A multivariate GARCH approach. Res. Int. Bus. Financ. 2008, 22, 247–266.
  39. Yarovaya, L.; Brzeszczyński, J.; Lau, C.K.M. Intra- and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures. Int. Rev. Financ. Anal. 2016, 43, 96–114.
Subjects: Business, Finance
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : ,
View Times: 122
Revisions: 2 times (View History)
Update Date: 13 Jul 2023